A clustering algorithm with affine space-based boundary detection

Clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster points before expanding to the boundary points. To achieve this, an affine space-based boundary detection algorithm was employed to divide data points into cluster boundary and internal points. A connection matrix was then formed by establishing neighbor relationships between internal and boundary points to perform clustering. Our clustering algorithm with an affine space-based boundary detection algorithm accurately detected clusters in datasets with different densities, shapes, and sizes. The algorithm excelled at dealing with high-dimensional datasets.